Adaptive smoothness constraint image multilevel fuzzy enhancement algorithm

For the problems of poor enhancement effect and long time consuming of the traditional algorithm, an adaptive smoothness constraint image multilevel fuzzy enhancement algorithm based on secondary color-to-grayscale conversion is proposed. By using fuzzy set theory and generalized fuzzy set theory, a new linear generalized fuzzy operator transformation is carried out to obtain a new linear generalized fuzzy operator. By using linear generalized membership transformation and inverse transformation, secondary color-to-grayscale conversion of adaptive smoothness constraint image is performed. Combined with generalized fuzzy operator, the region contrast fuzzy enhancement of adaptive smoothness constraint image is realized, and image multilevel fuzzy enhancement is realized. Experimental results show that the fuzzy degree of the image is reduced by the improved algorithm, and the clarity of the adaptive smoothness constraint image is improved effectively. The time consuming is short, and it has some advantages

Contours and contrast

Contrast in photographic and computer-generated imagery communicates colour and lightness differences that would be perceived when viewing the represented scene. Due to depiction constraints, the amount of displayable contrast is limited, reducing the image's ability to accurately represent the scene. A local contrast enhancement technique called unsharp masking can overcome these constraints by adding high-frequency contours to an image that increase its apparent contrast. In three novel algorithms inspired by unsharp masking, specialized local contrast enhancements are shown to overcome constraints of a limited dynamic range, overcome an achromatic palette, and to improve the rendering of 3D shapes and scenes. The Beyond Tone Mapping approach restores original HDR contrast to its tone mapped LDR counterpart by adding highfrequency colour contours to the LDR image while preserving its luminance. Apparent Greyscale is a multi-scale two-step technique that first converts colour images and video to greyscale according to their chromatic lightness, then restores diminished colour contrast with high-frequency luminance contours. Finally, 3D Unsharp Masking performs scene coherent enhancement by introducing 3D high-frequency luminance contours to emphasize the details, shapes, tonal range and spatial organization of a 3D scene within the rendering pipeline. As a perceptual justification, it is argued that a local contrast enhancement made with unsharp masking is related to the Cornsweet illusion, and that this may explain its effect on apparent contrast.Seit vielen Jahren ist die realistische Erzeugung von virtuellen Charakteren ein zentraler Teil der Computergraphikforschung. Dennoch blieben bisher einige Probleme ungelöst. Dazu zählt unter anderem die Erzeugung von Charakteranimationen, welche unter der Benutzung der traditionellen, skelettbasierten Ansätze immer noch zeitaufwändig sind. Eine weitere Herausforderung stellt auch die passive Erfassung von Schauspielern in alltäglicher Kleidung dar. Darüber hinaus existieren im Gegensatz zu den zahlreichen skelettbasierten Ansätzen nur wenige Methoden zur Verarbeitung und Veränderung von Netzanimationen. In dieser Arbeit präsentieren wir Algorithmen zur Lösung jeder dieser Aufgaben. Unser erster Ansatz besteht aus zwei Netz-basierten Verfahren zur Vereinfachung von Charakteranimationen. Obwohl das kinematische Skelett beiseite gelegt wird, können beide Verfahren direkt in die traditionelle Pipeline integriert werden, wobei die Erstellung von Animationen mit wirklichkeitsgetreuen Körperverformungen ermöglicht wird. Im Anschluss präsentieren wir drei passive Aufnahmemethoden für Körperbewegung und Schauspiel, die ein deformierbares 3D-Modell zur Repräsentation der Szene benutzen. Diese Methoden können zur gemeinsamen Rekonstruktion von zeit- und raummässig kohärenter Geometrie, Bewegung und Oberflächentexturen benutzt werden, die auch zeitlich veränderlich sein dürfen. Aufnahmen von lockerer und alltäglicher Kleidung sind dabei problemlos möglich. Darüber hinaus ermöglichen die qualitativ hochwertigen Rekonstruktionen die realistische Darstellung von 3D Video-Sequenzen. Schließlich werden zwei neuartige Algorithmen zur Verarbeitung von Netz-Animationen beschrieben. Während der erste Algorithmus die vollautomatische Umwandlung von Netz-Animationen in skelettbasierte Animationen ermöglicht, erlaubt der zweite die automatische Konvertierung von Netz-Animationen in so genannte Animations-Collagen, einem neuen Kunst-Stil zur Animationsdarstellung. Die in dieser Dissertation beschriebenen Methoden können als Lösungen spezieller Probleme, aber auch als wichtige Bausteine größerer Anwendungen betrachtet werden. Zusammengenommen bilden sie ein leistungsfähiges System zur akkuraten Erfassung, zur Manipulation und zum realistischen Rendern von künstlerischen Aufführungen, dessen Fähigkeiten über diejenigen vieler verwandter Capture-Techniken hinausgehen. Auf diese Weise können wir die Bewegung, die im Zeitverlauf variierenden Details und die Textur-Informationen eines Schauspielers erfassen und sie in eine mit vollständiger Information versehene Charakter-Animation umwandeln, die unmittelbar weiterverwendet werden kann, sich aber auch zur realistischen Darstellung des Schauspielers aus beliebigen Blickrichtungen eignet

Appearance-based image splitting for HDR display systems

High dynamic range displays that incorporate two optically-coupled image planes have recently been developed. This dual image plane design requires that a given HDR input image be split into two complementary standard dynamic range components that drive the coupled systems, therefore there existing image splitting issue. In this research, two types of HDR display systems (hardcopy and softcopy HDR display) are constructed to facilitate the study of HDR image splitting algorithm for building HDR displays. A new HDR image splitting algorithm which incorporates iCAM06 image appearance model is proposed, seeking to create displayed HDR images that can provide better image quality. The new algorithm has potential to improve image details perception, colorfulness and better gamut utilization. Finally, the performance of the new iCAM06-based HDR image splitting algorithm is evaluated and compared with widely spread luminance square root algorithm through psychophysical studies

Image synthesis based on a model of human vision

Modern computer graphics systems are able to construct renderings of such high quality that viewers are deceived into regarding the images as coming from a photographic source. Large amounts of computing resources are expended in this rendering process, using complex mathematical models of lighting and shading. However, psychophysical experiments have revealed that viewers only regard certain informative regions within a presented image. Furthermore, it has been shown that these visually important regions contain low-level visual feature differences that attract the attention of the viewer. This thesis will present a new approach to image synthesis that exploits these experimental findings by modulating the spatial quality of image regions by their visual importance. Efficiency gains are therefore reaped, without sacrificing much of the perceived quality of the image. Two tasks must be undertaken to achieve this goal. Firstly, the design of an appropriate region-based model of visual importance, and secondly, the modification of progressive rendering techniques to effect an importance-based rendering approach. A rule-based fuzzy logic model is presented that computes, using spatial feature differences, the relative visual importance of regions in an image. This model improves upon previous work by incorporating threshold effects induced by global feature difference distributions and by using texture concentration measures. A modified approach to progressive ray-tracing is also presented. This new approach uses the visual importance model to guide the progressive refinement of an image. In addition, this concept of visual importance has been incorporated into supersampling, texture mapping and computer animation techniques. Experimental results are presented, illustrating the efficiency gains reaped from using this method of progressive rendering. This visual importance-based rendering approach is expected to have applications in the entertainment industry, where image fidelity may be sacrificed for efficiency purposes, as long as the overall visual impression of the scene is maintained. Different aspects of the approach should find many other applications in image compression, image retrieval, progressive data transmission and active robotic vision

Computer Assisted Relief Generation - a Survey

In this paper we present an overview of the achievements accomplished to date in the field of computer aided relief generation. We delineate the problem, classify the different solutions, analyze similarities, investigate the evelopment and review the approaches according to their particular relative strengths and weaknesses. In consequence this survey is likewise addressed to researchers and artists through providing valuable insights into the theory behind the different concepts in this field and augmenting the options available among the methods presented with regard to practical application

Evaluation of the color image and video processing chain and visual quality management for consumer systems

With the advent of novel digital display technologies, color processing is increasingly becoming a key aspect in consumer video applications. Today’s state-of-the-art displays require sophisticated color and image reproduction techniques in order to achieve larger screen size, higher luminance and higher resolution than ever before. However, from color science perspective, there are clearly opportunities for improvement in the color reproduction capabilities of various emerging and conventional display technologies. This research seeks to identify potential areas for improvement in color processing in a video processing chain. As part of this research, various processes involved in a typical video processing chain in consumer video applications were reviewed. Several published color and contrast enhancement algorithms were evaluated, and a novel algorithm was developed to enhance color and contrast in images and videos in an effective and coordinated manner. Further, a psychophysical technique was developed and implemented for performing visual evaluation of color image and consumer video quality. Based on the performance analysis and visual experiments involving various algorithms, guidelines were proposed for the development of an effective color and contrast enhancement method for images and video applications. It is hoped that the knowledge gained from this research will help build a better understanding of color processing and color quality management methods in consumer video

A simplified HDR image processing pipeline for digital photography

High Dynamic Range (HDR) imaging has revolutionized the digital imaging. It allows capture, storage, manipulation, and display of full dynamic range of the captured scene. As a result, it has spawned whole new possibilities for digital photography, from photorealistic to hyper-real. With all these advantages, the technique is expected to replace the conventional 8-bit Low Dynamic Range (LDR) imaging in the future. However, HDR results in an even more complex imaging pipeline including new techniques for capturing, encoding, and displaying images. The goal of this thesis is to bridge the gap between conventional imaging pipeline to the HDR’s in as simple a way as possible. We make three contributions. First we show that a simple extension of gamma encoding suffices as a representation to store HDR images. Second, gamma as a control for image contrast can be ‘optimally’ tuned on a per image basis. Lastly, we show a general tone curve, with detail preservation, suffices to tone map an image (there is only a limited need for the expensive spatially varying tone mappers). All three of our contributions are evaluated psychophysically. Together they support our general thesis that an HDR workflow, similar to that already used in photography, might be used. This said, we believe the adoption of HDR into photography is, perhaps, less difficult than it is sometimes posed to be

Multi-task near-field perception for autonomous driving using surround-view fisheye cameras

Die Bildung der Augen führte zum Urknall der Evolution. Die Dynamik änderte sich von einem primitiven Organismus, der auf den Kontakt mit der Nahrung wartete, zu einem Organismus, der durch visuelle Sensoren gesucht wurde. Das menschliche Auge ist eine der raffiniertesten Entwicklungen der Evolution, aber es hat immer noch Mängel. Der Mensch hat über Millionen von Jahren einen biologischen Wahrnehmungsalgorithmus entwickelt, der in der Lage ist, Autos zu fahren, Maschinen zu bedienen, Flugzeuge zu steuern und Schiffe zu navigieren. Die Automatisierung dieser Fähigkeiten für Computer ist entscheidend für verschiedene Anwendungen, darunter selbstfahrende Autos, Augmented Realität und architektonische Vermessung. Die visuelle Nahfeldwahrnehmung im Kontext von selbstfahrenden Autos kann die Umgebung in einem Bereich von 0 - 10 Metern und 360° Abdeckung um das Fahrzeug herum wahrnehmen. Sie ist eine entscheidende Entscheidungskomponente bei der Entwicklung eines sichereren automatisierten Fahrens. Jüngste Fortschritte im Bereich Computer Vision und Deep Learning in Verbindung mit hochwertigen Sensoren wie Kameras und LiDARs haben ausgereifte Lösungen für die visuelle Wahrnehmung hervorgebracht. Bisher stand die Fernfeldwahrnehmung im Vordergrund. Ein weiteres wichtiges Problem ist die begrenzte Rechenleistung, die für die Entwicklung von Echtzeit-Anwendungen zur Verfügung steht. Aufgrund dieses Engpasses kommt es häufig zu einem Kompromiss zwischen Leistung und Laufzeiteffizienz. Wir konzentrieren uns auf die folgenden Themen, um diese anzugehen: 1) Entwicklung von Nahfeld-Wahrnehmungsalgorithmen mit hoher Leistung und geringer Rechenkomplexität für verschiedene visuelle Wahrnehmungsaufgaben wie geometrische und semantische Aufgaben unter Verwendung von faltbaren neuronalen Netzen. 2) Verwendung von Multi-Task-Learning zur Überwindung von Rechenengpässen durch die gemeinsame Nutzung von initialen Faltungsschichten zwischen den Aufgaben und die Entwicklung von Optimierungsstrategien, die die Aufgaben ausbalancieren.The formation of eyes led to the big bang of evolution. The dynamics changed from a primitive organism waiting for the food to come into contact for eating food being sought after by visual sensors. The human eye is one of the most sophisticated developments of evolution, but it still has defects. Humans have evolved a biological perception algorithm capable of driving cars, operating machinery, piloting aircraft, and navigating ships over millions of years. Automating these capabilities for computers is critical for various applications, including self-driving cars, augmented reality, and architectural surveying. Near-field visual perception in the context of self-driving cars can perceive the environment in a range of 0 - 10 meters and 360° coverage around the vehicle. It is a critical decision-making component in the development of safer automated driving. Recent advances in computer vision and deep learning, in conjunction with high-quality sensors such as cameras and LiDARs, have fueled mature visual perception solutions. Until now, far-field perception has been the primary focus. Another significant issue is the limited processing power available for developing real-time applications. Because of this bottleneck, there is frequently a trade-off between performance and run-time efficiency. We concentrate on the following issues in order to address them: 1) Developing near-field perception algorithms with high performance and low computational complexity for various visual perception tasks such as geometric and semantic tasks using convolutional neural networks. 2) Using Multi-Task Learning to overcome computational bottlenecks by sharing initial convolutional layers between tasks and developing optimization strategies that balance tasks